Knowledge Graph: Exploring Reasoning and Learning
Abstract: The topic of large scare knowledge representation and reasoning has been popular over the last two decades. It has gone through a few stages in recent years, including the Semantic Web, Linked Data and Knowledge Graph. Different stages come with different reasoning tasks; e.g., the Semantic Web stage favours ontological (schema) reasoning, while the Linked Data stage attracts data reasoning and query answering. In the Knowledge Graph stage, it seems that learning is regarded as a key reasoning task, at least as an approximate reasoning task. In this talk, I will share some of my thoughts on learning and reasoning in the Knowledge Graph stage, from the perspective of approximate reasoning, and maybe more.
Speaker: Dr Jeff Z. Pan is a Reader on Knowledge Graphs at the School of Informatics at The University of Edinburgh. He co-chairs the Knowledge Graphs group at the Alan Turing Institute. He received his Ph.D. in Computer Science from The University of Manchester. His research focuses primarily on knowledge representation and artificial intelligence, in particular on knowledge graph based learning and reasoning, and knowledge based natural language understanding and generations, as well as their applications. He was an official reviewer of the international Knowledge Graph standards RDF and SPARQL and was a key contributor of the international standard (OWL) of Knowledge Graph schemas. He led the development of the award-wining TrOWL approximate reasoner, which is one of the top three OWL 2 DL reasoners in the sound and complete Ontology Reasoner Evaluation (ORE2014). He was the Chief Scientist of the EU Marie-Curie K-Drive project. He is the Chief Editor of the first two books on Knowledge Graph. He is an Associate Editor of the Journal of Web Semantics (JWS) and of the International Journal on Semantic Web and Information Systems (IJSWIS). He is a Programme Chair of the 19th International Semantic Web Conference (ISWC 2020), the premier international forum for the Semantic Web and Knowledge Graph.